Robotic system with packing mechanism

ABSTRACT

A method for operating a robotic system includes determining package groupings for placing available packages on a platform; generating a two-dimensional (2D) placement plan based on discretized models representative of the available packages and the platform; generating a three-dimensional (3D) stacking plan based on the 2D placement plan; and implementing the 3D stacking plan for placing the available packages on the platform.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application contains subject matter related to U.S. patentapplication Ser. No. 16/428,714, filed May 31, 2019 titled “A ROBOTICSYSTEM WITH DYNAMIC PACKING MECHANISM,” which is incorporated herein byreference in its entirety.

This application contains subject matter related to U.S. patentapplication Ser. No. 16/428,809, filed May, 31, 2019, now issued as U.S.Pat. No. 10,618,172, titled “A ROBOTIC SYSTEM WITH ERROR DETECTION ANDDYNAMIC PACKING MECHANISM,” which is incorporated herein by reference inits entirety.

This application contains subject matter related to U.S. patentapplication Ser. No. 16/428,843, filed May 31, 2019, titled “ROBOTICSYSTEM FOR PROCESSING PACKAGES ARRIVING OUT OF SEQUENCE,” whichincorporated herein by reference in its entirety.

This application contains subject matter related to U.S. patentapplication Ser. No. 16/428,870 , filed May 31, 2019, now issued as U.S.Pat. No. 10,647,528, titled “ROBOTIC SYSTEM FOR PALLETIZING PACKAGESUSING REAL-TIME PLACEMENT SIMULATION,” which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present technology is directed generally to robotic systems and,more specifically, to systems, processes, and techniques for packingobjects.

BACKGROUND

With their ever-increasing performance and lowering cost, many robots(e.g., machines configured to automatically/autonomously executephysical actions) are now extensively used in many fields. Robots, forexample, can be used to execute various tasks (e.g., manipulate ortransfer an object through space) in manufacturing and/or assembly,packing and/or packaging, transport and/or shipping, etc. In executingthe tasks, the robots can replicate human actions, thereby replacing orreducing the human involvement that would otherwise be required toperform dangerous or repetitive tasks.

However, despite the technological advancements, robots often lack thesophistication necessary to duplicate human sensitivity and/oradaptability required for executing more complex tasks. For example,robots often lack the granularity of control and flexibility in theexecuted actions to account for deviations or uncertainties that mayresult from various real-world factors. Accordingly, there remains aneed for improved techniques and systems for controlling and managingvarious aspects of the robots to complete the tasks despite the variousreal-world factors.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an example environment in which a roboticsystem with a 3-dimensional packing mechanism may operate.

FIG. 2 is a block diagram illustrating the robotic system in accordancewith one or more embodiments of the present technology.

FIG. 3A is an illustration of discretized objects in accordance with oneor more embodiments of the present technology.

FIG. 3B is an illustration of discretized packing platform in accordancewith one or more embodiments of the present technology.

FIG. 3C is an illustration of a placement planning process in accordancewith one or more embodiments of the present technology.

FIGS. 4A-4C are illustrations of stacking rules in accordance with oneor more embodiments of the present technology.

FIG. 5A is an illustration of an example stacking plan in accordancewith one or more embodiments of the present technology.

FIG. 5B is an illustration of a stacking sequence in accordance with oneor more embodiments of the present technology.

FIG. 6 is a flow diagram for operating the robotic system of FIG. 1 inaccordance with one or more embodiments of the present technology.

DETAILED DESCRIPTION

Systems and methods for robotic systems with packing mechanisms aredescribed herein. A robotic system (e.g., an integrated system ofdevices that executes one or more designated tasks) configured inaccordance with some embodiments provides enhanced packing and storageefficiency by deriving optimal storage locations for objects andstacking them accordingly.

Traditional systems use offline packing simulators to predeterminepacking sequences/arrangements. The traditional packing simulatorsprocess object information (e.g., case shapes/sizes) for a predeterminedor estimated set of cases to generate packing plans. Once determined,the packing plans dictate and/or require specific placementlocations/poses of the objects at destinations (e.g., pallets, bins,cages, boxes, etc.), predefined sequences for the placement, and/orpredetermined motion plans. From the predetermined packing plans, thetraditional packing simulators may derive source requirements (e.g.,sequences and/or placements for the objects) that match or enable thepacking plans. Because the packing plans are developed offline intraditional systems, the plans are independent of actual packingoperations/conditions, object arrivals, and/or other systemimplementations. Accordingly, the overall operation/implementation willrequire the received packages (e.g., at the starting/pick up location)to follow fixed sequences that matches the predetermined packing plans.As such, traditional systems cannot adapt to real-time conditions and/ordeviations in the received packages (e.g., different sequence, location,and/or orientation), unanticipated errors (e.g., collisions and/or lostpieces), real-time packing requirements (e.g., received orders), and/orother real-time factors.

Further, because traditional systems group and pack objects according torigid predetermined plans/sequences, they require all objects at asource location to either (1) have a same dimension/type and/or (2)arrive according to a known sequence. For example, the traditionalsystems would require the objects to arrive (via, e.g., conveyor) at apickup location according to a fixed sequence. Also, for example, thetraditional systems would require the objects at the pickup location tobe placed at designated locations according to a predetermined pose. Assuch, traditional systems require one or more operations to order and/orplace the objects at the source (i.e., before the packing operation)according to the predetermined sequence/arrangement. Often, thetraditional systems require a sequence buffer, which costs upwards ofone million US dollars, to order and/or place the objects at the sourceaccording to the predetermined sequence/pose.

In contrast, the robotic system described herein can generate thepacking plans during system operation. The robotic system can generate areal-time and/or dynamic packing plan during the system operation basedon various real-time conditions. Real-time conditions can includecurrently existing or ongoing conditions, such as actual sourcesequences/locations/poses of objects, object conditions and/orrequirements, placement requirements, and/or other real-time factors.The robotic system can generate the packing plans in real-time, such asin response to a triggering event (e.g., a received order/request, ashipping schedule, and/or an operator input), according tocurrent/ongoing conditions and factors at the time of the packing planprocessing. In some embodiments, the packing plans can be dynamically(e.g., after initially starting one or more operations, such as theactual packing operation, begins) generated and/or adjusted, such as inresponse to a corresponding event (e.g., a re-evaluation timing, apacking/manipulation error, such as a collision or a lost piece, and/oroccurrence of other dynamic conditions).

Unlike the traditional systems, the robotic system described herein cangenerate the placement plans in real-time according to current/liveconditions (e.g., source sequences/locations/poses of objects, objectconditions and/or requirements, etc.). In some embodiments, the roboticsystem can generate the packing plan based on a discretization mechanism(e.g., a process, a circuit, a function, and/or a routine). For example,the robotic system can use the discretization mechanism to describephysical sizes/shapes of objects and/or target locations according to adiscretization unit (i.e., one discrete area/space). The robotic systemcan generate discretized object profiles that use the discretizationunits to describe the expected objects and/or discretized destinationprofiles that describe the target location (e.g., surface on top of thepallet and/or a space/bottom surface inside a bin/case/box).Accordingly, the robotic system can transform continuous real-worldspace/area into computer-readable digital information. Further, thediscretized data can allow a reduction in computational complexity fordescribing package footprint and for comparing various packageplacements. For example, package dimensions can correspond to integernumbers of discretization units, which lead to easier mathematicalcomputations, instead of real-world decimal number.

In some embodiments, the robotic system can generate the packing planbased on determining object groupings. The object groupings can be basedon object descriptions, such as customer-specified priorities, objectfragility measure (e.g., support weight limitations), object weight,object height, object type, and/or other aspects of the objects. Therobotic system can use the object groupings to generate and evaluate2-dimensional (2D) placement plans that include one or more objectgroupings. The robotic system can select the 2D placement plans thatsatisfy one or more conditions/rules and translate the selected 2Dplacement plans into three-dimensional (3D) mapping results. The 3Dmapping results can describe the heights of the 2D placement plans, suchas according to height measurements of the objects included in the 2Dplacement plans and their relative locations within the layer. Therobotic system can evaluate the 3D mapping results to verticallyorder/sequence to generate the 3D placement plans that include thevertical sequence for the 2D placement plans. In some embodiments, therobotic system can generate the 2D/3D placement plans for objects in aninitial state (e.g., before any objects are placed at the destinationzone) and/or for objects remaining in a non-packed state (e.g., afterone or more objects have been placed at the destination zone). Detailsregarding the object grouping and the placement plans are describedbelow.

The robotic system described below can utilize simplified andstream-lined processing architecture/sequence for real-timeimplementation. For example, the robotic system (via, e.g., a consumercomputing device, such as a desk top, a server, etc.) can generate thepacking plan based on real-time need (e.g., received order) and/orreal-time availability (e.g., shipping manifesto of incoming objectsand/or currently accessible objects) without utilizing the traditionalsequencer and simulator. When utilized in an offline context, such as toreplace the traditional sequencers and simulators, the robotic systemcan provide the offline packing plans using a simpler and cheapersolution.

Accordingly, the robotic system can improve efficiency, speed, andaccuracy for packing the objects based on adapting to the real-timeconditions. For example, the system described herein can generate theplacement plans that match/address the currently need (e.g., receivedorders), the current status (e.g., location, orientation, and/orquantity/availability) of packages, and/or the real-time status ofpreviously stacked/placed packages. As such, the robotic system canreceive and pack packages that are in various different/unexpectedquantities, locations, orientations, and/or sequences.

Further, the robotic system can reduce overall costs by eliminating theone or more operations, machines (e.g., sequence buffers), and/or humanassistance that would be necessary in traditional systems to order orplace the objects at the source and/or for the packing operation (e.g.,for error handling). By generating the packing plan according to theexisting package states (e.g., quantity, location, and/or orientation),the robotic system eliminates the need to reorganize or sequence thepackages, along with the associated machines/human operations, to meetthe requirements of traditional systems.

In the following description, numerous specific details are set forth toprovide a thorough understanding of the presently disclosed technology.In other embodiments, the techniques introduced here can be practicedwithout these specific details. In other instances, well-known features,such as specific functions or routines, are not described in detail inorder to avoid unnecessarily obscuring the present disclosure.References in this description to “an embodiment,” “one embodiment,” orthe like mean that a particular feature, structure, material, orcharacteristic being described is included in at least one embodiment ofthe present disclosure. Thus, the appearances of such phrases in thisspecification do not necessarily all refer to the same embodiment. Onthe other hand, such references are not necessarily mutually exclusiveeither. Furthermore, the particular features, structures, materials, orcharacteristics can be combined in any suitable manner in one or moreembodiments. It is to be understood that the various embodiments shownin the figures are merely illustrative representations and are notnecessarily drawn to scale.

Several details describing structures or processes that are well-knownand often associated with robotic systems and subsystems, but that canunnecessarily obscure some significant aspects of the disclosedtechniques, are not set forth in the following description for purposesof clarity. Moreover, although the following disclosure sets forthseveral embodiments of different aspects of the present technology,several other embodiments can have different configurations or differentcomponents than those described in this section. Accordingly, thedisclosed techniques can have other embodiments with additional elementsor without several of the elements described below.

Many embodiments or aspects of the present disclosure described belowcan take the form of computer- or processor-executable instructions,including routines executed by a programmable computer or processor.Those skilled in the relevant art will appreciate that the disclosedtechniques can be practiced on computer or processor systems other thanthose shown and described below. The techniques described herein can beembodied in a special-purpose computer or data processor that isspecifically programmed, configured, or constructed to execute one ormore of the computer-executable instructions described below.Accordingly, the terms “computer” and “processor” as generally usedherein refer to any data processor and can include Internet appliancesand handheld devices (including palm-top computers, wearable computers,cellular or mobile phones, multi-processor systems, processor-based orprogrammable consumer electronics, network computers, mini computers,and the like). Information handled by these computers and processors canbe presented at any suitable display medium, including a liquid crystaldisplay (LCD). Instructions for executing computer- orprocessor-executable tasks can be stored in or on any suitablecomputer-readable medium, including hardware, firmware, or a combinationof hardware and firmware. Instructions can be contained in any suitablememory device, including, for example, a flash drive and/or othersuitable medium.

The terms “coupled” and “connected,” along with their derivatives, canbe used herein to describe structural relationships between components.It should be understood that these terms are not intended as synonymsfor each other. Rather, in particular embodiments, “connected” can beused to indicate that two or more elements are in direct contact witheach other. Unless otherwise made apparent in the context, the term“coupled” can be used to indicate that two or more elements are ineither direct or indirect (with other intervening elements between them)contact with each other, or that the two or more elements cooperate orinteract with each other (e.g., as in a cause-and-effect relationship,such as for signal transmission/reception or for function calls), orboth.

Suitable Environments

FIG. 1 is an illustration of an example environment in which a roboticsystem 100 with a packing mechanism may operate. The robotic system 100can include and/or communicate with one or more units (e.g., robots)configured to execute one or more tasks. Aspects of the packingmechanism can be practiced or implemented by the various units.

For the example illustrated in FIG. 1, the robotic system 100 caninclude an unloading unit 102, a transfer unit 104 (e.g., a palletizingrobot and/or a piece-picker robot), a transport unit 106, a loading unit108, or a combination thereof in a warehouse or a distribution/shippinghub. Each of the units in the robotic system 100 can be configured toexecute one or more tasks. The tasks can be combined in sequence toperform an operation that achieves a goal, such as to unload objectsfrom a truck or a van and store them in a warehouse or to unload objectsfrom storage locations and prepare them for shipping. For anotherexample, the task can include placing the objects on a target location(e.g., on top of a pallet and/or inside a bin/cage/box/case). Asdescribed below, the robotic system can derive plans (e.g., placementlocations/orientations, sequence for transferring the objects, and/orcorresponding motion plans) for placing and/or stacking the objects.Each of the units can be configured to execute a sequence of actions(e.g., operating one or more components therein) to execute a task.

In some embodiments, the task can include manipulation (e.g., movingand/or reorienting) of a target object 112 (e.g., one of the packages,boxes, cases, cages, pallets, etc. corresponding to the executing task)from a start location 114 to a task location 116. For example, theunloading unit 102 (e.g., a devanning robot) can be configured totransfer the target object 112 from a location in a carrier (e.g., atruck) to a location on a conveyor belt. Also, the transfer unit 104 canbe configured to transfer the target object 112 from one location (e.g.,the conveyor belt, a pallet, or a bin) to another location (e.g., apallet, a bin, etc.). For another example, the transfer unit 104 (e.g.,a palletizing robot) can be configured to transfer the target object 112from a source location (e.g., a pallet, a pickup area, and/or aconveyor) to a destination pallet. In completing the operation, thetransport unit 106 can transfer the target object 112 from an areaassociated with the transfer unit 104 to an area associated with theloading unit 108, and the loading unit 108 can transfer the targetobject 112 (by, e.g., moving the pallet carrying the target object 112)from the transfer unit 104 to a storage location (e.g., a location onthe shelves). Details regarding the task and the associated actions aredescribed below.

For illustrative purposes, the robotic system 100 is described in thecontext of a shipping center; however, it is understood that the roboticsystem 100 can be configured to execute tasks in other environments/forother purposes, such as for manufacturing, assembly, packaging,healthcare, and/or other types of automation. It is also understood thatthe robotic system 100 can include other units, such as manipulators,service robots, modular robots, etc., not shown in FIG. 1. For example,in some embodiments, the robotic system 100 can include a depalletizingunit for transferring the objects from cage carts or pallets ontoconveyors or other pallets, a container-switching unit for transferringthe objects from one container to another, a packaging unit for wrappingthe objects, a sorting unit for grouping objects according to one ormore characteristics thereof, a piece-picking unit for manipulating(e.g., for sorting, grouping, and/or transferring) the objectsdifferently according to one or more characteristics thereof, or acombination thereof.

Suitable System

FIG. 2 is a block diagram illustrating the robotic system 100 inaccordance with one or more embodiments of the present technology. Insome embodiments, for example, the robotic system 100 (e.g., at one ormore of the units and/or robots described above) can includeelectronic/electrical devices, such as one or more processors 202, oneor more storage devices 204, one or more communication devices 206, oneor more input-output devices 208, one or more actuation devices 212, oneor more transport motors 214, one or more sensors 216, or a combinationthereof. The various devices can be coupled to each other via wireconnections and/or wireless connections. For example, the robotic system100 can include a bus, such as a system bus, a Peripheral ComponentInterconnect (PCI) bus or PCI-Express bus, a HyperTransport or industrystandard architecture (ISA) bus, a small computer system interface(SCSI) bus, a universal serial bus (USB), an IIC (I2C) bus, or anInstitute of Electrical and Electronics Engineers (IEEE) standard 1394bus (also referred to as “Firewire”). Also, for example, the roboticsystem 100 can include bridges, adapters, processors, or othersignal-related devices for providing the wire connections between thedevices. The wireless connections can be based on, for example, cellularcommunication protocols (e.g., 3G, 4G, LTE, 5G, etc.), wireless localarea network (LAN) protocols (e.g., wireless fidelity (WIFI)),peer-to-peer or device-to-device communication protocols (e.g.,Bluetooth, Near-Field communication (NFC), etc.), Internet of Things(IoT) protocols (e.g., NB-IoT, LTE-M, etc.), and/or other wirelesscommunication protocols.

The processors 202 can include data processors (e.g., central processingunits (CPUs), special-purpose computers, and/or onboard servers)configured to execute instructions (e.g. software instructions) storedon the storage devices 204 (e.g., computer memory). In some embodiments,the processors 202 can be included in a separate/stand-alone controllerthat is operably coupled to the other electronic/electrical devicesillustrated in FIG. 2 and/or the robotic units illustrated in FIG. 1.The processors 202 can implement the program instructions tocontrol/interface with other devices, thereby causing the robotic system100 to execute actions, tasks, and/or operations.

The storage devices 204 can include non-transitory computer-readablemediums having stored thereon program instructions (e.g., software).Some examples of the storage devices 204 can include volatile memory(e.g., cache and/or random-access memory (RAM)) and/or non-volatilememory (e.g., flash memory and/or magnetic disk drives). Other examplesof the storage devices 204 can include portable memory drives and/orcloud storage devices.

In some embodiments, the storage devices 204 can be used to furtherstore and provide access to processing results and/or predetermineddata/thresholds. For example, the storage devices 204 can store masterdata 252 that includes descriptions of objects (e.g., boxes, cases,and/or products) that may be manipulated by the robotic system 100. Inone or more embodiments, the master data 252 can include a dimension, ashape (e.g., templates for potential poses and/or computer-generatedmodels for recognizing the object in different poses), a color scheme,an image, identification information (e.g., bar codes, quick response(QR) codes, logos, etc., and/or expected locations thereof), an expectedweight, other physical/visual characteristics, or a combination thereoffor the objects expected to be manipulated by the robotic system 100. Insome embodiments, the master data 252 can include manipulation-relatedinformation regarding the objects, such as a center-of-mass (CoM)location on each of the objects, expected sensor measurements (e.g., forforce, torque, pressure, and/or contact measurements) corresponding toone or more actions/maneuvers, or a combination thereof. Also, forexample, the storage devices 204 can store object tracking data 254. Insome embodiments, the object tracking data 254 can include a log ofscanned or manipulated objects. In some embodiments, the object trackingdata 254 can include imaging data (e.g., a picture, point cloud, livevideo feed, etc.) of the objects at one or more locations (e.g.,designated pickup or drop locations and/or conveyor belts). In someembodiments, the object tracking data 254 can include locations and/ororientations of the objects at the one or more locations.

The communication devices 206 can include circuits configured tocommunicate with external or remote devices via a network. For example,the communication devices 206 can include receivers, transmitters,modulators/demodulators (modems), signal detectors, signalencoders/decoders, connector ports, network cards, etc. Thecommunication devices 206 can be configured to send, receive, and/orprocess electrical signals according to one or more communicationprotocols (e.g., the Internet Protocol (IP), wireless communicationprotocols, etc.). In some embodiments, the robotic system 100 can usethe communication devices 206 to exchange information between units ofthe robotic system 100 and/or exchange information (e.g., for reporting,data gathering, analyzing, and/or troubleshooting purposes) with systemsor devices external to the robotic system 100.

The input-output devices 208 can include user interface devicesconfigured to communicate information to and/or receive information fromhuman operators. For example, the input-output devices 208 can include adisplay 210 and/or other output devices (e.g., a speaker, a hapticscircuit, or a tactile feedback device, etc.) for communicatinginformation to the human operator. Also, the input-output devices 208can include control or receiving devices, such as a keyboard, a mouse, atouchscreen, a microphone, a user interface (UI) sensor (e.g., a camerafor receiving motion commands), a wearable input device, etc. In someembodiments, the robotic system 100 can use the input-output devices 208to interact with the human operators in executing an action, a task, anoperation, or a combination thereof.

The robotic system 100 can include physical or structural members (e.g.,robotic manipulator arms) that are connected at joints for motion (e.g.,rotational and/or translational displacements). The structural membersand the joints can form a kinetic chain configured to manipulate anend-effector (e.g., the gripper) configured to execute one or more tasks(e.g., gripping, spinning, welding, etc.) depending on the use/operationof the robotic system 100. The robotic system 100 can include theactuation devices 212 (e.g., motors, actuators, wires, artificialmuscles, electroactive polymers, etc.) configured to drive or manipulate(e.g., displace and/or reorient) the structural members about or at acorresponding joint. In some embodiments, the robotic system 100 caninclude the transport motors 214 configured to transport thecorresponding units/chassis from place to place.

The robotic system 100 can include the sensors 216 configured to obtaininformation used to implement the tasks, such as for manipulating thestructural members and/or for transporting the robotic units. Thesensors 216 can include devices configured to detect or measure one ormore physical properties of the robotic system 100 (e.g., a state, acondition, and/or a location of one or more structural members/jointsthereof) and/or of a surrounding environment. Some examples of thesensors 216 can include accelerometers, gyroscopes, force sensors,strain gauges, tactile sensors, torque sensors, position encoders, etc.

In some embodiments, for example, the sensors 216 can include one ormore imaging devices 222 (e.g., visual and/or infrared cameras, 2Dand/or 3D imaging cameras, distance measuring devices such as lidars orradars, etc.) configured to detect the surrounding environment. Theimaging devices 222 can generate representations of the detectedenvironment, such as digital images and/or point clouds, that may beprocessed via machine/computer vision (e.g., for automatic inspection,robot guidance, or other robotic applications). As described in furtherdetail below, the robotic system 100 (via, e.g., the processors 202) canprocess the digital image and/or the point cloud to identify the targetobject 112 of FIG. 1, the start location 114 of FIG. 1, the tasklocation 116 of FIG. 1, a pose of the target object 112, a confidencemeasure regarding the start location 114 and/or the pose, or acombination thereof.

For manipulating the target object 112, the robotic system 100 (via,e.g., the various circuits/devices described above) can capture andanalyze an image of a designated area (e.g., a pickup location, such asinside the truck or on the conveyor belt) to identify the target object112 and the start location 114 thereof. Similarly, the robotic system100 can capture and analyze an image of another designated area (e.g., adrop location for placing objects on the conveyor, a location forplacing objects inside the container, or a location on the pallet forstacking purposes) to identify the task location 116. For example, theimaging devices 222 can include one or more cameras configured togenerate images of the pickup area and/or one or more cameras configuredto generate images of the task area (e.g., drop area). Based on thecaptured images, as described below, the robotic system 100 candetermine the start location 114, the task location 116, the associatedposes, a packing/placement plan, a transfer/packing sequence, and/orother processing results. Details regarding the packing algorithm aredescribed below.

In some embodiments, for example, the sensors 216 can include positionsensors 224 (e.g., position encoders, potentiometers, etc.) configuredto detect positions of structural members (e.g., the robotic arms and/orthe end-effectors) and/or corresponding joints of the robotic system100. The robotic system 100 can use the position sensors 224 to tracklocations and/or orientations of the structural members and/or thejoints during execution of the task.

Discretization Model Processing

FIG. 3A and FIG. 3B are illustrations of discretized data used to planand pack objects in accordance with one or more embodiments of thepresent technology. FIG. 3A illustrates discretized objects and FIG. 3Billustrates discretized packing platform for the object packing andplanning thereof. For example, the robotic system 100 of FIG. 1 (via,e.g., the processors 202 of FIG. 2) can map continuous surfaces/edges ofreal-world objects (e.g., packages, pallets, and/or other objectsassociated with the task) into discrete counterparts (e.g., unit lengthsand/or unit areas). Also, the robotic system 100 can include discretizedmodels/representations of the expected objects stored in the master data252 of FIG. 2.

In some embodiments, as illustrated in FIG. 3A, the robotic system 100can use discretized object models 302 to plan/derive stacking placementsof objects. The discretized object models 302 (shown using dotted lines)can represent exterior physical dimensions, shapes, edges, surfaces, ora combination thereof (shown using solid lines) for known and/orexpected objects (e.g., packages, boxes, cases, etc.) according to adiscretization unit (e.g., a unit length). In some embodiments, asillustrated in FIG. 3B, the robotic system 100 can use one or morediscretized platform models 304 to plan/derive stacking placements ofobjects. The discretized platform models 304 can represent a placementsurface (e.g., a top surface of the pallet) according to thediscretization unit. In some embodiments, the discretization unit caninclude a length that is preset by a system operator, a system designer,a predetermined input/setting, or a combination thereof.

In some embodiments, the discretized platform models 304 can include topviews of one or more standard size pallets (e.g., 1.1 m by 1.1 mpallets). Accordingly, the discretized platform models 304 cancorrespond to pixelated 2D representations of the pallet top surfacesalong a horizontal plane (e.g., the x-y plane) according to a gridsystem utilized by the robotic system 100. In some embodiments, thediscretized object models 302 can include top views (e.g., x-y plane, asillustrated on the left side in FIG. 3A) and/or horizontal/profile views(e.g., x-z plane, as illustrated on the right side) for the objectsexpected/known by the robotic system 100. Accordingly, the discretizedobject models 302 can correspond to pixelated 2D/3D representations ofthe objects.

As an illustrative example, the robotic system 100 can use unit pixels310 (e.g., polygons, such as squares, having one or more dimensionsaccording to the discretization unit) to describe areas/surfaces oftargeted objects (via, e.g., the discretized object models 302) andloading platforms (via, e.g., the discretized platform models 304).Accordingly, the robotic system 100 can pixelate the objects and theloading platforms along the x-y axes. In some embodiments, the size ofthe unit pixels 310 (e.g., the discretization unit) can change accordingto dimensions of the objects and/or dimensions of the loading platforms.The size of the unit pixels 310 can also be adjusted (via, e.g., apreset rule/equation and/or operator selection) to balance requiredresources (e.g., computation times, required memory, etc.) with packingaccuracy. For example, when the size decreases, the computation timesand the packing accuracy can increase. Accordingly, discretization ofthe packing tasks (e.g., the target packages and the packing platforms)using adjustable unit pixels 310 provides increased flexibility forpalletizing the packages. The robotic system 100 can control a balancebetween the computation resources/time with the packing accuracyaccording to unique scenarios, patterns, and/or environments.

For the examples illustrated in FIG. 3A and FIG. 3B, the robotic system100 can expect/process objects corresponding to a first package type321, a second package type 322, a third package type 323, a fourthpackage type 324, and/or a fifth package type 325. The robotic system100 can plan and place/stack the packages on a placement pallet 340 thatcorresponds to the task location 116 of FIG. 1. For the placementplanning, the robotic system 100 can generate and/or utilize thediscretized object models 302 including a first object model 331, asecond object model 332, a third object model 333, a fourth object model334, and/or a fifth object model 335 that respectively represent thecorresponding packages using the unit pixels 310. Similarly, the roboticsystem 100 can generate and/or utilize the discretized platform model304 for the placement pallet 340 using the unit pixels 310.

In some embodiments, the robotic system 100 can round up (e.g., for thediscretized object models 302, such as for the third object model 333and/or the fourth object model 334) the actual dimension of the objectsuch that the unit pixels 310 extend beyond the actual peripheral edgesof the object. In some embodiments, the robotic system 100 can rounddown (e.g., for the discretized platform models 304) the actualdimensions of the platform surface such that the unit pixels 310 areoverlapped and/or contained within the actual peripheral edges of theobject.

Based on the discretized data/representations, the robotic system 100can generate a placement plan 350 for placing/packing the packages ontothe placement pallet 340. The placement plan 350 can include plannedlocations on the placement pallet 340 for the targeted packages. Therobotic system 100 can generate the placement plan 350 for placing oneor more of available packages designated for loading/palletization. Forexample, the robotic system 100 can generate the placement plan 350 forstacking a set of packages from the available packages (e.g., receivedpackages and/or outgoing ordered packages).

The robotic system 100 can generate the placement plan 350 according toa set of placement rules, placement conditions, parameters,requirements, etc. In some embodiments, the robotic system 100 cangenerate the placement plan 350 based on packages grouped according tothe set, such as according to the package types (e.g., package types321-325), package heights, customer specified priority, fragility (e.g.,maximum supported weight, such as for packages stacked thereon), weightrange, or a combination thereof. In some embodiments, the robotic system100 can generate the placement plan 350 according to stackingconditions, such as, e.g., stacking the taller packages further awayfrom the depalletizing unit. Other examples of the placement rules,conditions, parameters, requirements, etc. can include packagedimensions, collision free requirement, stack stability, the groupingconditions (e.g., package types, package heights, priority, etc.),package separation requirements or the absence thereof, maximization oftotal loaded packages, or a combination thereof. Details regarding theplacement planning is described below.

For the example illustrated in FIG. 3B, the robotic system 100 cangenerate the 2D placement plan (e.g., the placement plan 350) for a setof packages that correspond to the packages types 321-325. The roboticsystem 100 can generate the placement plan 350 that places threepackages of the first package type 321, four packages of the secondpackage type 322, four packages of the third package type 323, fivepackages of the fourth package type 324, and four packages of the fifthpackage type 325. The placement plan 350 can group the packages tomaximize adjacent groupings of packages with similar height (e.g., equalor within a threshold limit from each other). Accordingly, the roboticsystem 100 can group the four of the second package type 322 in a 2×2arrangement located at the lower left-hand corner of the placementpallet 340. A second grouping of packages (e.g., the packages of thefirst package type 321, the fourth package type 324, and the fifthpackage type 325) can be placed around the initially placed group.Accordingly, the continuous surface area for the first grouping (e.g.,at a height of four unit pixels 310) and the surface area for the secondgrouping (e.g., at a height of two unit pixels 310) can be maximized.Also, the robotic system 100 can separate the packages of the thirdpackage type 323 based on one or more requirements, such as fragility(e.g., limiting the number of supported items) and/or separationrequirements. Similarly, the robotic system 100 can generate the 2Dplacement plan according to boundary requirements (e.g., one or more ofthe unit pixels 310 from the edge of the placement pallet 340).

In some embodiments, the robotic system 100 can generate the placementplan 350 based on 2D planning (e.g., x-y footprint, such as a top-view)and/or 3D planning (e.g., x-z or y-z footprint, such as a profile-view).For example, the robotic system 100 can generate the placement plan 350based on iteratively deriving potential 2D placements along the x-yplane, testing the potential placements according to the placementrules, conditions, etc., calculating a placement score, or a combinationthereof. The robotic system 100 can generate the placement plan 350based on selecting the 2D placement plan that optimizes (e.g., highestor lowest) the placement score. In some embodiments, the robotic system100 can use the 2D placement plan to further generate a 3D plan (e.g.,stacking plan; not shown in FIG. 3B). For example, the robotic system100 can generate the 3D placement plan based on using the 2D placementplan as a layer within a stack. In other words, the robotic system 100can place the generated 2D placement over/on top of one or more layers(e.g., other 2D placement plans) and/or under/below one or more otherlayers.

As an illustrative example, the robotic system 100 can estimate andconsider heights of the placed objects in deriving the 2D plans. Forexample, the robotic system 100 can pixelate the object heights (e.g.,stored in the master data) as shown in FIG. 3D. Also, the robotic system100 can map the predetermined height data of the placed object to eachof the unit pixels occupied by the object. With the heights mapped toeach of the pixels, the robotic system 100 derive placement surfaces ofthe resulting 2D placement plan 350. The placement surfaces can eachcorrespond to a derived surface/plane that can have, and support objectsplaced thereon, such as due same or similar heights of objects formingthe derived surface.

The robotic system 100 can derive placement surfaces based onidentifying groupings of unit pixels that have height values that arewithin a threshold range of each other. In some embodiments, the roboticsystem 100 can derive the placement surfaces based on identifying amaximum height for the placement plan 350. Based on the maximum height,the robotic system 100 can identify the unit pixels in the placementplan 350 having heights matching or within a threshold range from themaximum height. The robotic system 100 can derive an outline based onconnecting corners and/or extending edges of outermost/perimeter unitpixels with qualifying heights to derive the placement surface. Therobotic system 100 can recursively repeat the process for regionsoutside of the placement areas using lower heights. For the exampleillustrated in FIG. 3B, the robotic system 100 can derive a firstplacement surface 352, a second placement surface 354, and a thirdplacement surface 356. The first placement surface 352 can correspond tothe rectangular area shown in the lower left corner of the placementplan 350 with the maximum height of four unit pixels. The secondplacement surface 354 can correspond to the surrounding area (shownusing dashed lines) with height of two unit pixels. The third placementsurface 356 can correspond to the separate area on the right side of theplacement plan 350 with the height of one unit pixel. Details for the 2Dand 3D placement planning are described below.

FIG. 3C is an illustration of a placement planning process in accordancewith one or more embodiments of the present technology. The roboticsystem 100 (via, e.g., the one or more processors 202 of FIG. 2) canderive the placement plan 350 of FIG. 3B for a set of available packages362. The available packages 362 can correspond to the objects that needto be packed for an egress shipment and/or storage. For example, theavailable packages 362 can correspond to incoming objects received viaan ingress shipment and/or stored objects that have been ordered for anegress shipment. In some embodiments, the robotic system 100 can use ashipping manifest, an order list, etc. to identify the availablepackages 362 in real-time, such as directly in response to (i.e., withina threshold duration from) receiving the manifest, the list, etc.Accordingly, the robotic system 100 can use the identified availablepackages 362 to derive the placement plan 350 in real-time. As such, therobotic system 100 can use real-time conditions, availability, and/ordemands to derive the placement plan 350 instead of off-line packingsimulators that utilize a hypothetical number/set/combination ofpackages to derive plans that are applied regardless of real-timeconditions. In some embodiments, the robotic system 100 can use devices(e.g., one or more of the processors 202) located at the locationreceiving, storing, and/or sending the objects, such as a shipping huband/or a warehouse.

In some embodiments, as discussed in detail below, the robotic system100 can group and/or sequence the available packages 362. The roboticsystem 100 can use the ordered set of the available packages 362 toderive the placement plan 350. The robotic system 100 can determine andevaluate unique placement locations/combinations for the availablepackages 362 to derive the placement plan 350. In other words, therobotic system 100 can determine a set of potential placementcombinations 364 and evaluate (e.g., score) them according a set ofpredetermined requirements, conditions, weights, costs, subsequentimplications, or a combination thereof. Based on the evaluation, therobotic system 100 can select a placement combination to derive theplacement plan 350.

In at least one embodiment, the robotic system 100 can derive theplacement plan 350 using an algorithm that iteratively evaluatesplacements of the sequenced packages. As illustrated in FIG. 3C, forexample, the robotic system 100 can begin the derivation by determiningan initial placement for the first package in the available packages362. Accordingly, the robotic system 100 can overlap the correspondingdiscretized object model 302 of FIG. 3A over the discretized platformmodel 304 of FIG. 3B at an initial location (e.g., a corner, a middlelocation, and/or another preset location). The robotic system 100 cantrack remaining packages 372 based on removing the placed package (e.g.,the first package) from the available packages 362.

Based on the initial placement, the robotic system 100 can determine aset of possible placements for the second package in the availablepackages 362. The robotic system 100 can determine the set of possibleplacements according to a predetermined rule, pattern, or a combinationthereof. For example, the robotic system 100 can determine the placementlocations according to a pattern of locations relative to the previouslyplaced package(s) (e.g., relative to the previously placed package(s)).Also, the robotic system 100 can determine the placement locations basedon a minimum/maximum separation distance or a lack thereof requiredbetween one or more of the packages. Further, the robotic system 100 candetermine the placement locations based on rotating the package (i.e.,the corresponding discretized object model 302) according to apredetermined amount, such as 90 degrees. In some embodiments, therobotic system 100 can limit the placement possibilities according to apredetermined threshold and/or pattern. Further, the robotic system 100can update the remaining packages 372 accordingly.

The robotic system 100 can repeat the above-described process anditeratively process the available packages 362 until a stoppingcondition is reached. Some examples of the stopping condition canrepresent that all packages have been placed (i.e., the remainingpackages 372 is empty), the placements cannot be improved (e.g., sameevaluation score as the previous tier/iteration), no more packages canbe placed over the discretized platform model 304, or a combinationthereof.

In some embodiments, the robotic system 100 can track the possibleplacements and the corresponding potential placement combinations 364using a search tree 374. A root of the search tree 374 can correspond tothe initial placement and each level or tier can include potentialplacements of the subsequent package in the available packages 362. Thedifferent tiers can be connected to form a branch that corresponds to aunique combination of placements for the set of packages.

For potential placements of each package, the robotic system 100 canidentify and eliminate (e.g., represented by ‘X’ in FIG. 3C) redundantfootprints. For example, at each tier of the search tree 374, therobotic system 100 can compare (e.g., overlay) the resulting footprintsof the potential placement locations/combinations. Based on thecomparison, the robotic system 100 can eliminate duplicates of theresulting footprints. In some embodiments, the robotic system 100 canfurther compare transposed, rotated, and/or mirrored versions of theresulting footprints to eliminate related duplicates. For example, therobotic system 100 can rotate one footprint by 90 degrees and/ortranspose the footprint across one or more mirroring lines (e.g. adiagonal line extending across opposing corners, a bisecting line(s)extending along x and/or y directions, or a combination thereof) andcompare it to other footprints.

Also, for potential placements of each package, the robotic system 100can identify and eliminate placements that violate one or morerequirements/constraints. One example of the requirements/constraintscan be based on collision probabilities. The robotic system 100 cancalculate an approach path for each placement location and acorresponding collision probability according to the pre-existingfootprint, one or more dimensions of the packages, a location of thetransfer robot, a previous event or history, or a combination thereof.The robotic system 100 can eliminate the placements where the collisionprobability exceeds a predetermined threshold. Another example of therequirements/constraints can be a supported weight for stacking (i.e.,placing directly on/over one or more support packages) the package. Forone or more of the packages under the placement location, the roboticsystem 100 can calculate a support weight (i.e., a combined weight ofpackages or portions thereof directly over) based on the weight of theplaced package. The robotic system 100 can eliminate the placementswhere the support weight violates (e.g., exceeds or is within athreshold range from) a fragility requirement (e.g., a maximumsupportable weight) for one or more of the packages under the placementlocation.

In some embodiments, the robotic system 100 can track and/or evaluatethe placement combinations 364 using a priority queue 376 (e.g., a heapstructure etc.). The priority queue 376 can order the placementcombinations 364 according to a sequence of preferences. The roboticsystem 100 can evaluate or score each of the placement combinations 364according to one or more predetermined criteria. The criteria caninclude one or more costs associated with already placed items and/orone or more heuristic scores associated with how the current placementaffects future placements or possibilities.

One example of the criteria can include maximization of footprintdensity. The robotic system 100 can calculate the footprint density foran outer perimeter 382 for a grouping of packages. In some embodiments,the outer perimeter 382 can be determined based on exposed/outerperimeter edges of the grouping of packages. The robotic system 100 canfurther enclose surrounding/related areas by extending two or more edgesand finding an intersect and/or by drawing a line that connects one ormore corners of the footprint. The robotic system 100 can calculate thefootprint density as a ratio between an actual occupied area 384 (e.g.,a number of unit pixels 310 corresponding to the shaded area) and anempty area 386 (e.g., a number of unit pixels 310 corresponding to theenclosed/related areas). The robotic system 100 can be configured toprefer (e.g., by assigning a higher/lower score) to placement plans thatminimize the empty area 386.

Stacking Rules

FIGS. 4A-4C are illustrations of stacking rules in accordance with oneor more embodiments of the present technology. The robotic system 100can use the stacking rules to place packages on top of each other, suchas for stacking/placing one or more layers of packages above one or moreother layer(s) of packages. The robotic system 100 can use the stackingrules for improving stability of the stacked packages and prevent anypackages from slipping and/or tipping during movement of the pallet. Forillustrative purposes, FIGS. 4A-4C show a top package 452 directly aboveand supported by (e.g., directly contacting) one or more supportpackages 454.

FIG. 4A illustrates a horizontal offset rule 402 used to generate 3Dplacements (e.g., the 3D placement plan 350). The horizontal offset rule402 can include a regulation, a requirement, or a combination thereoffor controlling horizontal offsets of vertical edges/surfaces betweenstacked items. For example, the horizontal offset rule 402 can be basedon an overlap requirement 422, an overhang requirement 424, or acombination thereof. The overlap requirement 422 can include a minimumamount (e.g., a percentage or a ratio of length, width, and/or surfacearea) of overlap between the stacked packages. In some embodiments, theoverlap requirement 422 can require that a minimum amount of horizontaldimension/surface area of the top package 452 is overlapped with that ofthe support package 454. The overhang requirement 424 can include amaximum amount (e.g., a percentage or a ratio of length, width, and/orsurface area) of overhang, such as a portion of the top package 452 thathorizontally extends past a perimeter edge/surface of the supportpackage 454.

In some embodiments, the horizontal offset rule 402 can be based onweight, dimension, and/or center-of-mass (CoM) locations 412. Forexample, the overlap requirement 422 and/or the overhang requirement 424can be based on the CoM locations 412, such as for evaluating a distancebetween the CoM locations 412 of the top package 452 and the supportpackage 454 relative to a distance between the top CoM location and ahorizontal edge/surface of the support package 454 and/or an overhangdistance (e.g. a measure along a horizontal direction of a portion ofthe top package 452 extending past peripheral edge(s) of the supportpackage 454). In some embodiments, the horizontal offset rule 402 can bebased on a CoM offset requirement 426 that requires the CoM locations412 of the top packages 452 and the support packages 454 to be within athreshold. The threshold can include a predetermined distance, athreshold limit for a ratio between the offset distance between the CoMlocations 412 relative to a horizontal dimension, an overhang distance,an overlapped distance, or a combination thereof.

FIG. 4B illustrates a support separation rule 404 used to generate 3Dplacements (e.g., a stacking plan). The support separation rule 404 caninclude a regulation, a requirement, or a combination thereof forcontrolling a horizontal separation distance 414 between the supportpackages 454. The horizontal separation distance 414 can correspond to ahorizontal distance between peripheral surfaces/edges of adjacentsupport packages 454. In some embodiments, the support separation rule404 can be further based on locations and/or amounts of overlappedsurfaces between the top package 452 and the support packages 454. Forexample, the support separation rule 404 can require that the horizontalseparation distance 414 to be larger than any overhang distances by apredetermined percentage. Also, the support separation rule 404 canrequire that the horizontal separation distance 414 extends under theCoM location 412 of the top package 452.

FIG. 4C illustrates a vertical offset rule 406 used to generate 3Dplacements (e.g., the 3D placement plan 350). The vertical offset rule406 can include a regulation, a requirement, or a combination thereoffor controlling a support height difference 416 between verticallocations of the supporting packages 454. The support height difference416 can correspond to a vertical distance between top portions ofcorresponding support packages 454, such as for portions that wouldlikely contact the top package 452 placed over the corresponding supportpackages 454. In some embodiments, the vertical offset rule 406 canrequire the support height difference 416 to be under a predeterminedthreshold requirement for stacking one or more packages on top of thesupporting packages 454. In some embodiments, the support separationrule 404 can vary based on the layer height. For example, when the toppackage 452 (e.g., the supported package) is part of the top-most layer,the limit for the support height difference 416 can be greater than forthe lower layers.

The robotic system 100 can generate stacking plans (e.g., a 3Dcombination of multiple 2D placement plans) according to the stackingrules. For example, the robotic system 100 can generate the 2D placementplans (e.g., the placement plan 350 of FIG. 3B) according to heightrequirements (e.g., for keeping the heights of the package groupingswithin a threshold distance). Subsequently, the robotic system 100 cangenerate the stacking plans based on vertically overlapping (e.g.,stacking) the 2D placement plans.

Stacking Sequence

FIG. 5A is an illustration of an example of a stacking plan 502 (e.g., aplan representing a 3D mapping of the available packages and/or theplacement plans 350 correspond to layers within the 3D mapping) inaccordance with one or more embodiments of the present technology. Forillustrative purposes, the stacking plan 502 is illustrated using afirst layer 512, a second layer 514, and a third layer 516 for a firststack 520 of the packages (e.g., e.g., at least the packages 1-1 to 1-4,2-1 to 2-2, and 3-1 to 3-3). Each of the first layer 512, the secondlayer 514, and the third layer 516 can be an instance of the placementplan 350. The first layer 512 can be on the bottom such that thepackages (e.g., at least the packages 1-1, 1-2, 1-3, and 1-4) thereindirectly contact the placement pallet 340. The packages (e.g., at leastthe packages 2-1 and 2-2) in the second layer 514 can be directly on(i.e. having direct contact with) and above the first layer 512.Similarly, the packages (e.g., at least the packages 3-1 and 3-2) of thethird layer 516 can be directly on and contact the second layer 514.

As discussed in detail below, the robotic system 100 can plan each ofthe layers separately while considering vertical parameters (e.g.,supported weight, layer height, etc.). In generating the stacking plan502, the robotic system 100 can vertically combine and/or sequence theseparate layers according to the vertical parameters and/or the stackingrules. In some embodiments, the robotic system 100 can plan the layersaccording to vertical placement of the packages. For example, therobotic system 100 can generate the first layer 512 as including allpackages that directly contact the placement pallet 340, such asincluding the bottom two packages in a second stack 522. Also, therobotic system 100 can plan the package labeled ‘3-3’ as part of thesecond layer 514. In some embodiments, the robotic system 100 canre-plan and/or adjust the layers (e.g., the placement plan 350) ingenerating the stacking plan 502. For example, the robotic system 100can adjust the layers to facilitate the stacking/placement sequence. Asillustrated in FIG. 5A, the robotic system 100 can adjust the layerssuch that the second stack 522 is considered a separate stack (i.e.,separate from the first, second, and third layers 512-516). Accordingly,the robotic system 100 can be free to plan and/or stack the packages ofthe second stack 522 separately/differently from the layers of the firststack 520.

Also, in some embodiments, the robotic system 100 can move largerpackages closest to the transfer unit 104 of FIG. 1 (e.g., thepalletizing robot) to a higher layer to facilitate stacking sequence.Assuming that the transfer unit 104 is to the right of the placementpallet 340 illustrated in FIG. 5A, the ‘3-3’ package can become anobstacle (i.e., due to its height) if it is placed before packageslabeled ‘3-1’ and ‘3-2’. Accordingly, the robotic system 100 can adjustthe layers such that the ‘3-3’ package is part of a higher layer (e.g.,the third layer 516 instead of the second layer 512). As a result, whenthe robotic system 100 places the packages according to the layers, the‘3-3’ package can be placed after the ‘3-1’ and ‘3-2’ packages.

In other alternative embodiments, the robotic system 100 can separatelycalculate the stacking or placement sequences based on analyzing thestacking plan 502 without being bound to the layers. For discussionpurposes, FIG. 5B is an illustration of a stacking sequence 530 (e.g.,an identification of a placing order for the available packages) that isnot bound by stacking of packages according to the layers in accordancewith one or more embodiments of the present technology. The stackingsequence 530 can be for placing a stacked package 532 above a supportingpackage and horizontally between two end packages. The stacking sequence530 can be such that the package (labeled ‘1’) furthest from thetransfer unit 104 (not illustrated in FIG. 5B, assumed to be located tothe right of the placement pallet 340) can be placed first and thesecond package (labeled ‘2’) is placed on the placement pallet 340afterwards. The robotic system 100 can calculate the stacking sequence530 such that the stacked package 532 (labeled ‘3’) is placed before(e.g., third) one of the end packages 534 (labeled ‘4’). As describedabove, the robotic system 100 can calculate the stacking sequence 530based on adjusting the one of the end packages 534 to belong to a secondlayer with the stacked package 532 or based on independently calculatingthe stacking order from the stacking plan 502.

Operational Flow

FIG. 6 is a flow diagram for a method 600 of operating the roboticsystem 100 of FIG. 1 in accordance with one or more embodiments of thepresent technology. The method 600 can be for generating 2D/3D packingplans for placing packages (e.g., cases and/or boxes) on to a platform(e.g., a pallet) and/or for placing the packages accordingly. The method600 can be implemented based on executing the instructions stored on oneor more of the storage devices 204 of FIG. 2 with one or more of theprocessors 202 of FIG. 2.

At block 602, the robotic system 100 can identify a package set (e.g.,the available packages 362 of FIG. 3C) and a destination (e.g., the tasklocation 116 of FIG. 1, such as a pallet and/or a container forreceiving the packages). For example, the robotic system 100 canidentify the package set to represent the available packages 362including packages that are available for packing, located at a source,designated for placement, and/or listed in an order/request/manifest.Also, the robotic system 100 identify a size or a dimension of an area(e.g., a top loading surface of the pallet, such as the placement pallet340 of FIG. 3) of the task location 116 where the packages can beplaced. In some embodiments, the robotic system 100 can identify a size,a dimension, a type, or a combination thereof for a pallet.

At block 604, the robotic system 100 can generate and/or accessdiscretized models (e.g., the discretized object models 302 of FIG. 3Aand/or the discretized platform models 304 of FIG. 3B) corresponding tothe package set that represent the available packages 362 and/or thetask location 116. In some embodiments, the robotic system 100 cangenerate (e.g., in real-time, such as after receiving the order and/orprior to beginning the packing operation, or offline) the discretizedmodels based on dividing physical dimensions of the objects and/or theplatform area (e.g., the pallet top surface according to the unit pixel310 of FIG. 3B). The unit pixel 310 can be predetermined (by, e.g., amanufacturer, an ordering customer, and/or an operator), such as at 1millimeters (mm) or 1/16 inches (in) or greater (e.g., at 5 mm or 20mm). In some embodiments, the unit pixel 310 can be based (e.g., apercentage or a fraction) on a dimension or a size of one or more of thepackages and/or the platform.

In some embodiments, the robotic system 100 can access the discretizedmodels stored in the storage devices 204 and/or another device (e.g., astorage device, a database, and/or a server of a package supplieraccessed via the communication devices 206 of FIG. 2). The roboticsystem 100 can access the predetermined discretized models thatrepresents the available packages 362 and/or the task location 116. Forexample, the robotic system 100 can access the discretized object models302 corresponding to the available packages 362 by searching the masterdata 252 of FIG. 2 (e.g., a predetermined table or a lookup table) forthe available packages and their corresponding models. Similarly, therobotic system 100 can access the discretized platform model 304representing the platform, such as the identified pallet, where theavailable packages are to be placed.

At block 606, the robotic system 100 can determine package groupings(e.g., subgroupings of the available packages). The robotic system 100can determine the package groupings based on the available packages 362for placing them on the identified platform (e.g., the placement pallet340). The robotic system 100 can determine the package groupingsaccording to similarities and/or patterns in one or more characteristicsof the available packages 362. In some embodiments, as illustrated atblock 621, the robotic system 100 can determine the package grouping bygrouping the available packages 362 according to groupingconditions/requirements. Some examples of the groupingconditions/requirements can include a package priority (e.g., asspecified by one or more customers), a fragility rating (e.g., a maximumweight supportable by the package), a weight, a package dimension (e.g.,a package height), a package type, or a combination thereof. In groupingthe available packages 362, the robotic system 100 can search the masterdata 252 for the various characteristics of the available packages 362that match the grouping conditions/requirements.

At block 608, the robotic system 100 can calculate a processing order(e.g., a sequence for considering/deriving placement locations) for theavailable packages 362 and/or the groupings thereof (i.e., the packagegroupings). In some embodiments, as illustrated at block 622, therobotic system 100 can calculate the processing order according to oneor more sequencing conditions/requirements. For example, the roboticsystem 100 can prioritize placement planning of the package groupingsaccording to a number of packages within each of the groupings, such asfor processing the package groupings with greater number of packagesearlier in the placement planning. In some embodiments, the sequencingconditions can overlap with the grouping conditions, such as for theweight ranges, the fragility ratings, etc. For example, the roboticsystem 100 can prioritize the processing of the heavier and/or the lessfragile packages for earlier processing and/or for placement in lowerlayers.

In some embodiments, the robotic system 100 can prioritize the placementplanning according to a combined horizontal area. The robotic system 100can calculate (via, e.g., multiplying corresponding widths and lengths)or access surface areas of top surfaces of the packages in the groupingsusing information specified in the master data 252. In calculating thecombined horizontal area, the robotic system 100 can add the surfaceareas of packages having the same type and/or heights within a thresholdrange. In some embodiments, the robotic system 100 can prioritize theplacement planning of groupings that have the larger combined horizontalarea for earlier processing and/or for placement in lower layers.

For one or more embodiments, the robotic system 100 can load a bufferwith identifiers and/or quantities of the available packages 362. Therobotic system 100 can sequence the identifiers in the buffer accordingto the groupings. Further, the robotic system 100 can sequence theidentifiers in the buffer according to the processing order.Accordingly, the sequenced values in the buffer can correspond to theavailable packages 362 and/or the remaining packages 372 illustrated inFIG. 3C.

As illustrated at block 624, for example, the robotic system 100 cancalculate the processing order for an initial set (e.g., the packageset) of the available packages 362 before implementing the correspondingstacking plan 502 of FIG. 5, such as before any of the packages in thepackage set is placed on the platform. In some embodiments, asillustrated at block 626, the robotic system 100 can calculate theprocessing order for a remaining set of the available packages 362 afterinitiating or while implementing the corresponding stacking plan 502.For example, as illustrated by a feedback loop from block 616, therobotic system 100 can calculate the processing order for the remainingset (e.g., a portion of the available packages 362 that have not beentransferred to the platform and/or remain at a source location)according to one or more triggering conditions. Example triggeringconditions can include stacking errors (e.g., lost or fallen packages),collision events, predetermined retriggering timings, or a combinationthereof.

At block 610, the robotic system 100 can generate 2D plans (e.g., theplacement plans 350 of FIG. 3B) for placing the available packages 362along a horizontal plane. For example, the robotic system 100 cangenerate the placement plans 350 to represent the 2D mappings of theavailable packages 362 along the horizontal plane. The robotic system100 can generate two or more placement plans based on the discretizedmodels. For example, the robotic system 100 can generate the placementplans 350 based on comparing the discretized object models 302 to thediscretized platform model 304. The robotic system 100 can determinedifferent placements/arrangements of the discretized object models 302,overlap/compare them to the discretized platform model 304, andvalidate/retain the arrangements that are within the boundaries of thediscretized platform model 304 when overlapped. The robotic system 100can designate the packages that cannot be placed within the boundariesof the discretized platform model 304 for another layer (e.g., anotherinstance of the placement plans 350). Accordingly, the robotic system100 can iteratively derive placement locations for the placement plans350 that represent 2D layers of the stacking plan 502 until each of thepackages in the package set have been assigned a location in theplacement plans 350.

In some embodiments, the robotic system 100 can generate the placementplans 350 based on the package groupings. For example, the roboticsystem 100 can determine the arrangements for the packages within onepackage grouping before considering placements of packages in anothergrouping. When packages within a package grouping over flows a layer(i.e., cannot fit in one layer or one instance of the discretizedplatform model 304) and/or after placing all packages of one grouping,the robotic system 100 can assign locations for the packages in the nextgrouping to any remaining/unoccupied areas in the discretized platformmodel 304. The robotic system 100 can iteratively repeat the assignmentsuntil none of the unassigned packages can fit over remaining spaces ofthe discretized platform model 304.

Similarly, the robotic system 100 can generate the placement plans 350based on the processing order (e.g., based on the package groupingsaccording to the processing order). For example, the robotic system 100can determine a test arrangement based on assigning packages and/orgroupings according to the processing order. The robotic system 100 canassign the earliest sequenced package/grouping an initial placement forthe test arrangement, and then test/assign the subsequentpackages/groupings according to the processing order. In someembodiments, the robotic system 100 can retain the processing order forthe packages/groupings across layers (e.g., across instances of theplacement plans 350). In some embodiments, the robotic system 100 canrecalculate and update (illustrated using dashed feedback line in FIG.6) the processing order after each layer is filled.

In some embodiments, as an illustrative example of the above describedprocesses, the robotic system 100 can generate the 2D plans byidentifying the different package types (e.g., the first, second, third,fourth, and/or the fifth package type 321-325 of FIG. 3A, respectively)within the package set. In other words, at block 632, the robotic system100 can identify unique packages (e.g., as represented by the packagetypes) within each of the package grouping and/or the package set.

At block 634, the robotic system 100 can derive (e.g., iteratively)placement locations for each of the available packages 362. At block636, the robotic system 100 can determine an initial placement locationfor the unique package first in sequence according to the processingorder. The robotic system 100 can determine the initial placementlocation according to a predetermined pattern as described above. Insome embodiments, the robotic system 100 can calculate initialplacements for each unique package. The resulting initial placements caneach be developed into a unique placement combination (e.g., an instanceof the search tree 374 of FIG. 3C), such as by tracking the placementplan 350 across iterations. At block 638, the robotic system 100 canderive and track candidate placement locations for the subsequentpackages according to the processing order and/or the remaining packages372 as described above. Accordingly, the robotic system 100 caniteratively derive the placement combinations 364 of FIG. 3C.

In deriving the placement combinations 364 (e.g., candidate placementlocations), the robotic system 100 can test/evaluate locations of thediscretized object model 302 of the corresponding package based oniteratively deriving and evaluating candidate stacking scenarios (e.g.,potential combinations of unique placement locations for the availablepackages). The candidate stacking scenarios can each be derived based onidentifying unique potential locations (e.g., according to apredetermined sequence/rule for placement locations) for the packagesaccording to the above discussed sequence. The candidate stackingscenarios and/or the unique placement locations can be evaluatedaccording to one or more placement criteria (e.g., requirements,constraints, placement costs, and/or heuristic scores). For example, theplacement criteria can require that the discretized object models 302entirely fit within horizontal boundaries of the discretized platformmodel 304 when placed at the selected location. Also, the placementcriteria can require that placement of the discretized object models 302be within or over a threshold distance relative to the initial placementlocation (e.g. such as along a horizontal direction) and/or the previousplacement location, such as for adjacent placements or separationrequirements. Other examples of the placement criteria can includepreferences for adjacently placing packages having smallestdifference(s) in one or more package dimensions (e.g., height), thefragility ratings, the package weight ranges, or a combination thereof.In some embodiments, the placement criteria can include collisionprobabilities that can correspond to locations and/or characteristics(e.g., height) of previously assigned packaged in the layer relative toa reference location (e.g., location of the palletizing robot).Accordingly, the robotic system 100 can generate multiple uniqueplacement combinations (i.e., candidate placement plans for each layerand/or the candidate stacking scenarios that each layer includesmultiple layers) of package placement locations. In some embodiments,the robotic system 100 can track the placements of the combination basedon generating and updating the search tree 374 across the placementiterations.

At block 640, the robotic system 100 can calculate/update a placementscore for each combination/package placement. The robotic system 100 cancalculate the placement score according to one or more of the placementconditions/preferences (e.g., package dimensions, collisionprobabilities, fragility ratings, package weight ranges, separationrequirements, package quantity conditions). For example, the roboticsystem 100 can use preference factors (e.g., multiplier weights) and/orequations to describe a preference for: separation distances betweenpackages, differences in package dimensions/fragility ratings/packageweights for adjacent packages, the collision probabilities,continuous/adjacent surfaces at the same height, a statistical resultthereof (e.g., average, maximum, minimum, standard deviation, etc.), ora combination thereof. Each combination can be scored according to thepreference factors and/or the equations that may be predefined by asystem manufacturer, an order, and/or a system operator. In someembodiments, the robotic system 100 can calculate the placement score atthe end of the overall placement iterations.

In some embodiments, the robotic system 100 can update the sequence ofthe placement combinations 364 in the priority queue 376 of FIG. 3Cafter each placement iteration. The robotic system 100 can update thesequence based on the placement score.

The robotic system 100 can stop the placement iterations, such as whenone candidate placement plan is finished, based on determining an emptysource status, a full layer status, or an unchanged score status. Theempty source status can represent that all of the available packageshave been placed. The full layer status can represent that no otherpackage can be placed in the remaining areas of the considereddiscretized platform model 304. The unchanged score status can representthat the placement score for the combination remains constant across oneor more consecutive placement iterations. In some embodiments, therobotic system 100 can repeat the placement iterations using differentinitial placement locations and/or different processing order (e.g., forreordering groups having same sequencing value/score associated with thesequencing conditions) to derive other instances of the candidatestacking scenarios. In other words, the robotic system 100 can generatemultiple 2D placement plans, where each 2D placement plan can representa layer within a 3D stack (e.g., an instance of the candidate stackingscenarios). In other embodiments, the robotic system 100 can iterativelyconsider the 3D effect as a 2D placement plan is derived and beginderiving the next layer as a next iteration when the 2D placement planbecomes full.

At block 612, the robotic system 100 can generate a stacking plan (e.g.,the stacking plan 502). In some embodiments, the robotic system 100 canbegin generating the stacking plan 502 when the placement location ofthe processed package overlaps one or more previously placed/processedpackages.

In generating the stacking plan 502 and/or assessing the 2D plans, therobotic system 100 can convert each of the placement combinations 364and/or the placement plans into 3D states as illustrated at block 652.For example, the robotic system 100 can assign the height values for thepackages to the placement combinations 364. In other words, the roboticsystem 100 can generate a contour map (an estimate of a depth map) basedon the adding the package heights to placement combinations 364.

With the 3D states, the robotic system 100 can evaluate the placementcombinations 364 according to one or more stacking rules (e.g., thehorizontal offset rule 402 of FIG. 4A, the support separation rule 404of FIG. 4B, and/or the vertical offset rule 406 of FIG. 4C). As anillustrative example, when the placed package is stacked on/over one ormore previously processed packages, the robotic system 100 can eliminateany of the placement combinations 364 that violate the overlaprequirement 422 of FIG. 2, the overhang requirement 424 of FIG. 4A, thevertical offset rule 406, the CoM offset requirement 426 of FIG. 4A, ora combination thereof described above. In one or more embodiments, therobotic system 100 can eliminate any of the placement combinations 364that violate fragility ratings of one or more packages under theprocessed package, such as by estimating the supported weights at theoverlapped packages and comparing them to the corresponding fragilityratings.

For the remaining placement combinations 364, the robotic system 100 cancalculate 3D placement scores or update the placement score, such asillustrated at block 654. The robotic system 100 can use predeterminedpreferences (e.g., weights and/or equations) associated with placementcosts and/or heuristic values for 3D placements. The predetermined 3Dpreferences can be similar to the 2D preferences, grouping preferences,sequencing conditions, or a combination thereof. For example, the 3Dpreferences can be configured to calculate collision probabilities basedon the 3D state and to calculate scores that favor the placementcombinations with lower collision probabilities. Also, the roboticsystem 100 can calculate the scores based on the remaining packages 372,sizes of support areas with common height, number of packed items in the3D state, difference between the heights of the processed packages, or acombination thereof. In some embodiments, the robotic system 100 canupdate the sequence of the placement combinations 364 in the priorityqueue 376 according to the scores.

After the 3D states have been processed, the robotic system 100 canupdate the 2D plans by deriving a placement for the next package in theremaining packages 372, such as at block 610. The robotic system 100 canrepeat the above-described process until a stopping condition, such aswhen all of the available packages 362 have been processed (i.e., emptyvalue/set for the remaining packages 372) and/or when the placementcombinations 364 cannot be improved (also referred to as unimprovedcombinations). Some examples of unimproved combinations can include whenthe currently processed placement eliminates the last of the placementcombinations 364 in the priority queue 376 due to one or more of theviolations and/or when the placement score remains constant for thepreferred combinations across a threshold number of iterations.

When the stopping condition is detected, such as at block 656, therobotic system 100 can select one of the derived placement combinations364 according to the placement scores (e.g., the 2D and/or the 3Drelated scores). Accordingly, the robotic system 100 can designate theselected placement combination as the stacking plan 502 (e.g., a set ofthe placement plans 350).

In some embodiments, as an illustrative example, the robotic system 100can implement the functions of block 610 and 612 differently. Forexample, at block 610, the robotic system 100 can generate the 2D plan(e.g., an instance of the placement plan 350) for a bottom layer asdescribed above. In doing so, the robotic system 100 can be configuredto place heavier preference (e.g., greater parameter weights) formatching package heights, heavier package weights and/or greatersupportable weight for the packages in considering the placements and/orthe processing order. The robotic system 100 can derive the first 2Dplan for the base layer as described above for block 610.

Once the first 2D layer is complete/full as described above, therebyforming the base layer, the robotic system 100 can convert the placementplan into 3D states as described for block 612/652. Using the 3Dinformation, the robotic system 100 can identify one or more planarsections/areas (e.g., the placement surfaces 352-356 of FIG. 3B) of thebase layer as described above. Using the planar sections, the roboticsystem 100 can iteratively/recursively derive package placements for thenext layer above the base layer. The robotic system 100 can considereach of the planar sections as new instances of the discretized platformmodels 304 and test/evaluate different placements as described above forblock 610. In some embodiments, the robotic system 100 can derive the 2Dplacements using the placement surfaces but calculate the score acrossthe entirety of the placement pallet 340. Accordingly, the roboticsystem 100 can be configured to follow preferences for larger placementareas for subsequent layers without being limited to the precedingplacement areas.

Once the iterative placement process stops for the second layer, therobotic system 100 can calculate planar sections (e.g., top surfaceshaving heights within a threshold range) for the derived layer togenerate the 2D placements of the remaining packages/groupings for thenext above layer. The iterative layering process can continue until thestopping condition has been met as described above.

In some embodiments, the robotic system 100 can separately generate 2Dplans (e.g., two or more of the placement plans 350) at block 612. Therobotic system 100 can generate the stacking plan 502 based onvertically combining (e.g., arranging/overlapping the 2D placement plansalong a vertical direction) the 2D plans.

At block 614, the robotic system 100 can calculate a packing sequence(e.g., the stacking sequence 530 of FIG. 5B) based on the stacking plan502. As an example, the packing sequence can be for identification ofthe placing order of the available packages 362. In some embodiments, asillustrated at block 662, the robotic system 100 can calculate thepacking sequence layer-by-layer. In other words, the robotic system 100can calculate the packing sequence for each layer and then connect thesequences according to the order/position of the layers from bottom totop. In calculating the packing sequence, in some embodiments, therobotic system 100 can adjust the placement plans as illustrated atblock 672. For example, the robotic system 100 can adjust the placementplans by reassigning one or more of the packages (e.g., packages withheights that increase the collision probabilities for subsequentmanipulations/transfers) from a lower-layer placement plan to ahigher-layer placement plan. Any packages supported by the reassignedpackage can also be reassigned to a further higher layer. In otherwords, the reassigned packages can remain at the same horizontalplacement and be associated with a higher layer, such that the packagescan be placed later as illustrated in FIG. 5B. At block 674, the roboticsystem 100 can calculate the packing sequence (e.g., the stackingsequence 530) based on the adjusted placement plan, such as bypacking/manipulating objects that are assigned in the higher layersafter the objects assigned in the lower layers.

In other embodiments, as illustrated at block 664, the robotic system100 can calculate the packing sequence regardless/independent of thelayer assignments. In other words, the robotic system 100 can calculatethe packing sequence such that packages assigned to a lower layer may beplaced after packages assigned to a higher layer.

In calculating the packing sequence, both within or across layers, therobotic system 100 can analyze the locations of the packages in thestacking plan 502 according to one or more package dimensions (e.g.,heights), relative placement locations, or a combination thereof. Forexample, the robotic system 100 can sequence placements of boxes furtheraway from a unit/reference location (e.g., location of the palletizingrobot) before closer assigned packages. Also, the robotic system 100 canplace the taller/heavier packages earlier when their assigned locationsare along the perimeters of the placement plan and away from the unitlocation.

At block 616, the robotic system 100 can implement the stacking plan 502for placing the available packages 362 on the platform. The roboticsystem 100 can implement the stacking plan 502 based on communicatingone or more motion plans, actuator commands/settings, or a combinationthereof to the corresponding device/unit (e.g., the transfer unit 104 ofFIG. 1, the actuation devices 212 of FIG. 2, the sensors 216 of FIG. 2,etc.) according to the stacking plan 502. The robotic system 100 canfurther implement the stacking plan 502 based on executing thecommunicated information at the devices/units to transfer the availablepackages 362 from a source location to the destination platform.Accordingly, the robotic system 100 can place the available packages 362according to the 3D mapping, where one or more of the available packages362 are placed/stacked on top of other packages, such as placing theavailable packages 362 layer-by-layer. Further, the robotic system 100can manipulate/transfer the packages according to the packing sequence.As such, the robotic system 100 can place the packages layer-by-layer orwithout such restrictions as described above.

Discretization of the tasks and the 2D/3D layering described aboveprovides improved efficiency, speed, and accuracy for packing objects.Accordingly, the reduction in operator inputs and the increase inaccuracy can further decrease human labor for the automated packingprocess. In some environments, the robotic system 100 as described abovecan eliminate the necessity of sequencing buffers, which can cost aroundor over $1 million US.

CONCLUSION

The above Detailed Description of examples of the disclosed technologyis not intended to be exhaustive or to limit the disclosed technology tothe precise form disclosed above. While specific examples for thedisclosed technology are described above for illustrative purposes,various equivalent modifications are possible within the scope of thedisclosed technology, as those skilled in the relevant art willrecognize. For example, while processes or blocks are presented in agiven order, alternative implementations may perform routines havingsteps, or employ systems having blocks, in a different order, and someprocesses or blocks may be deleted, moved, added, subdivided, combined,and/or modified to provide alternative or sub-combinations. Each ofthese processes or blocks may be implemented in a variety of differentways. Also, while processes or blocks are at times shown as beingperformed in series, these processes or blocks may instead be performedor implemented in parallel, or may be performed at different times.Further, any specific numbers noted herein are only examples;alternative implementations may employ differing values or ranges.

These and other changes can be made to the disclosed technology in lightof the above Detailed Description. While the Detailed Descriptiondescribes certain examples of the disclosed technology as well as thebest mode contemplated, the disclosed technology can be practiced inmany ways, no matter how detailed the above description appears in text.Details of the system may vary considerably in its specificimplementation, while still being encompassed by the technologydisclosed herein. As noted above, particular terminology used whendescribing certain features or aspects of the disclosed technologyshould not be taken to imply that the terminology is being redefinedherein to be restricted to any specific characteristics, features, oraspects of the disclosed technology with which that terminology isassociated. Accordingly, the invention is not limited, except as by theappended claims. In general, the terms used in the following claimsshould not be construed to limit the disclosed technology to thespecific examples disclosed in the specification, unless the aboveDetailed Description section explicitly defines such terms.

Although certain aspects of the invention are presented below in certainclaim forms, the applicant contemplates the various aspects of theinvention in any number of claim forms. Accordingly, the applicantreserves the right to pursue additional claims after filing thisapplication to pursue such additional claim forms, in either thisapplication or in a continuing application.

We claim:
 1. A method for operating a robotic system, the methodcomprising: implementing a stacking plan for placing available packageson a platform using an end effector of a robotic arm and according to athree-dimensional (3D) mapping with one or more of the availablepackages placed on top of one or more other packages, whereinimplementing the stacking plan includes: identifying a package setrepresenting the available packages designated for placement on theplatform; accessing discretized object models corresponding to thepackage set, wherein the discretized object models each representphysical dimensions, shapes, or a combination thereof of a package typein the available packages; accessing a discretized platform modelrepresenting the platform where the available packages are to be placed;determining, using at least one processor of the robotic system, packagegroupings based on the available packages, wherein the package groupingseach represent a subgrouping of the available packages; calculating,using the at least one processor, a processing order for the packagegroupings; generating, using the at least one processor, placement plansbased on the package groupings according to the processing order,wherein the placement plans represent two-dimensional (2D) mappings ofthe available packages along a horizontal plane; and generating, usingthe at least one processor, the stacking plan based on converting theplacement plans into 3D states, wherein the stacking plan represents the3D mapping of the available packages and the placement plans correspondto layers within the 3D mapping.
 2. The method of claim 1, whereinimplementing the stacking plan further includes: calculating a packingsequence based on the stacking plan, wherein the packing sequence is foridentifying a placing order for the available packages; and implementingthe stacking plan according to the packing sequence for transferring andplacing the available packages.
 3. The method of claim 2, wherein:calculating the packing sequence includes: adjusting the placement plansby reassigning one or more of the available packages from a lower-layerplacement plan to a higher-layer placement plan, and calculating thepacking sequence for each placement plan; and implementing the stackingplan further includes implementing the stacking plan for placing theavailable packages layer-by-layer.
 4. The method of claim 2, whereincalculating the packing sequence includes analyzing the stacking planaccording to heights of the available packages, placement locations ofthe available packages relative to a unit location, or a combinationthereof.
 5. The method of claim 1, wherein determining the packagegroupings includes grouping the available packages according tospecified priorities, fragility ratings, package weight ranges, heights,package types, or a combination thereof for the available packages. 6.The method of claim 1, wherein calculating the processing order includesprioritizing the package groupings according to a number of packagestherein, a combined horizontal area, weight ranges, fragility ratings,or a combination thereof.
 7. The method of claim 6, wherein calculatingthe processing order includes calculating the processing order for aninitial set of the available packages before implementing the stackingplan.
 8. The method of claim 6, wherein calculating the processing orderincludes calculating the processing order for a remaining set of theavailable packages after or while implementing the stacking plan.
 9. Themethod of claim 1, wherein: generating the placement plans includes:identifying package types within the available packages; and iterativelyderiving placement locations for each of the available packages basedon: determining an initial placement location for one of the packagetypes, and deriving candidate placement locations for a next packagebased on one or more placement conditions; and the placement plans eachrepresent a unique placement combination of the initial placementlocation and the candidate placement locations of one or more of theavailable packages.
 10. The method of claim 9, wherein the one or moreplacement conditions include package dimensions, collisionprobabilities, fragility ratings, package weight ranges, separationrequirements, package quantity conditions, or a combination thereof. 11.The method of claim 9, wherein iteratively deriving placement locationsincludes: tracking the placement plans across iterations; calculatingplacement scores for the placement plans according to the one or moreplacement conditions; and sequencing the placement plans according tothe placement scores.
 12. The method of claim 11, wherein tracking thecombinations includes: generating and updating a search tree includingthe placement plans; and stopping the iterations based on determining anempty source status or an unchanged score status.
 13. The method ofclaim 9, wherein the candidate placement locations are within athreshold distance from the initial placement location along ahorizontal direction.
 14. The method of claim 1, wherein generating thestacking plan includes generating the stacking plan according tostacking rules regarding an overlap between a top package and a supportpackage, an overhang of the top package over the support package, acenter-of-mass (CoM) location of the top package relative to one or moresupport packages, a separation distance between two or more supportpackages, a difference in heights of two or more support packages, or acombination thereof.
 15. The method of claim 1, wherein: generating theplacement plans and generating the stacking plan include derivingcandidate stacking scenarios; each candidate stacking scenario isrecursively or iteratively derived based on: generating a lower 2D planfor forming a lower layer, identifying one or more planar sections ofthe first candidate 2D plan, wherein each planar section representsadjacent and/or continuous horizontal top surfaces, generating an upper2D plan based on testing placement of remaining package groupingsrelative to the one or more planar sections, calculating a placementscore for the candidate stacking scenario at least based on a number ofplaced packages, and stopping the iterations or the recursions based ondetermining an empty source status, a full layer status, or an unchangedscore status; and generating the stacking plan includes generating thestacking plan based on selecting one of the candidate stacking scenariosaccording to the placement score.
 16. A robotic system comprising: atleast one processor; and at least one memory device communicativelyconnected to the at least one processor and having stored thereoninstructions that, when executed by the at least one processor, causethe robotic system to implement a stacking plan for placing availablepackages on a platform using an end effector of a robotic arm andaccording to a three-dimensional (3D) mapping with one or more of theavailable packages placed on top of one or more other packages, wherein,to implement the stacking plan, the robotic system is configured to:identify a package set representing the available packages designatedfor placement on the platform; access discretized object modelscorresponding to the package set, access a discretized platform modelrepresenting the platform where the available packages are to be placed;determine package groupings based on the available packages, wherein thepackage groupings each represent a subgrouping of the availablepackages; calculate a processing order for the package groupings;generate two or more two-dimensional (2D) placement plans based on thepackage groupings according to the processing order, wherein each of the2D placement plans is for placing the available packages along ahorizontal plane; and generate the stacking plan based on the two ormore 2D placement plans, wherein the stacking plan represents the 3Dmapping and includes the two or more 2D placement plans arranged along avertical direction.
 17. A tangible, non-transient computer-readablemedium having processor instructions stored thereon that, when executedby one or more processors of a robotic system, cause the robotic systemto implement a method, the method comprising: identifying a package setrepresenting the available packages designated for placement on theplatform; accessing discretized object models corresponding to thepackage set, wherein the discretized object models each representphysical dimensions, shapes, or a combination thereof of a package typein the available packages; accessing a discretized platform modelrepresenting the platform where the available packages are to be placed;determining package groupings based on the available packages, whereinthe package groupings each represent a subgrouping of the availablepackages; calculating a processing order for the package groupings;generating placement plans based on the package groupings according tothe processing order, wherein the placement plans representtwo-dimensional (2D) mappings of the available packages along ahorizontal plane; generating a stacking plan based on converting theplacement plans into three-dimensional (3D) states, wherein the stackingplan represents a 3D mapping of the available packages and the placementplans correspond to layers within the 3D mapping; and implementing thestacking plan for placing the available packages on the platform usingan end effector of a robotic arm and according to the 3D mapping withone or more of the available packages placed on top of one or more otherpackages.
 18. The tangible, non-transient computer-readable medium ofclaim 17, wherein the method further comprises: calculating a packingsequence based on the stacking plan, wherein the packing sequence is foridentifying a placing order for the available packages; and implementingthe stacking plan according to the packing sequence for transferring andplacing the available packages.
 19. The tangible, non-transientcomputer-readable medium of claim 18, wherein: determining the packagegroupings includes grouping the available packages according tospecified priorities, fragility ratings, package weight ranges, heights,package types, or a combination thereof for the available packages;calculating the processing order includes prioritizing the packagegroupings according to a number of packages therein, a combinedhorizontal area, the package weight ranges, the fragility ratings, or acombination thereof; and generating the placement plans includes:identifying package types within the available packages, and iterativelyderiving placement locations for each of the available packages basedon: determining an initial placement location for one of the packagetypes; deriving candidate placement locations for a next item based onone or more placement conditions; and sequencing the placement plansaccording to the one or more placement conditions.
 20. The tangible,non-transient computer-readable medium of claim 18, wherein: generatingthe placement plans and generating the stacking plan include: convertingthe placement plans into 3D states based on height information for thepackages added in the placement plans, and calculating placement scoresfor the placement plans based on the 3D states and 3D placementpreferences or criteria; and generating the stacking plan furtherincludes generating the stacking plan based on selecting one of theplacement plans according to the placement scores.